Audio Filtering on the command line and Python

Nipun Batra

Introduction

In this post I will look into some filters for audio processing in ffmpeg, sox, and Python. I have recorded a small 6 second audio clip where for the first couple of seconds I was not speaking, but background noise is present.

I had recorded the audio on my Apple device and it was default recorded in .m4a format. I convert it to the wav format. I use ffmpeg for the same. In addition, I am using two flags: -v quiet to reduce the amount of information printed on the console. Second, I am using -y to overwrite an existing file with the same name.

!ffmpeg -i Test.m4a Test.wav -v quiet -y

from IPython.display import Audio
import matplotlib.pyplot as plt
%matplotlib inline

Audio("Test.wav")

!ffmpeg -i Test.wav -lavfi showspectrumpic=s=720x540:color='magma' ../images/input-spectogram.png -y -v quiet

As can be seen in the above image, I am speaking somewhere close to 3.70 seconds onwards. However, the audio is pretty noisy before this even though I am not speaking. This is due to the background noise coming in from the fans and the air conditioning system.

!sox Test.wav -n spectrogram -o ../images/sox-sg.png

!sox Test.wav -n rate 32k spectrogram  -o ../images/sox-sg-trimmed.png

I’ll now get some attributes of the post that are required for processing, such as the recording rate. ## Getting attributes of the recorded file

!ffmpeg -i Test.wav

ffmpeg version 4.4 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple clang version 12.0.5 (clang-1205.0.22.9)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.4_2 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-avresample --enable-videotoolbox
  libavutil      56. 70.100 / 56. 70.100
  libavcodec     58.134.100 / 58.134.100
  libavformat    58. 76.100 / 58. 76.100
  libavdevice    58. 13.100 / 58. 13.100
  libavfilter     7.110.100 /  7.110.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  9.100 /  5.  9.100
  libswresample   3.  9.100 /  3.  9.100
  libpostproc    55.  9.100 / 55.  9.100
[0;33mGuessed Channel Layout for Input Stream #0.0 : mono
[0mInput #0, wav, from 'Test.wav':
  Metadata:
    title           : Test
    encoder         : Lavf58.76.100
  Duration: 00:00:06.63, bitrate: 768 kb/s
  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s
[4;31mAt least one output file must be specified
[0m

As can be seen from the cell above, the recording rate is 48 kHz. We will need this when we do some processing in Python.

Building a noise profile from first 3 second

!ffmpeg -i Test.wav -ss 0 -to 3.5 -c copy Noise-Test.wav -v quiet -y

Audio('Noise-Test.wav')

!sox Noise-Test.wav -n rate 32k spectrogram  -o ../images/sox-noise.png

!sox Noise-Test.wav -n noiseprof noise.prof

!sox Noise-Test.wav Noise-Test-cleaned.wav noisered noise.prof 0.21

Audio("Noise-Test-cleaned.wav")

!sox Test.wav Test-cleaned-05.wav noisered noise.prof 0.05


!sox Test.wav Test-cleaned-18.wav noisered noise.prof 0.18
!sox Test.wav Test-cleaned-21.wav noisered noise.prof 0.21

Audio("Test-cleaned-05.wav")

Audio("Test-cleaned-18.wav")

Audio("Test-cleaned-21.wav")

!sox Test-cleaned-21.wav -n rate 32k spectrogram  -o ../images/sox-cleaned-21.png

!sox Test-cleaned-05.wav -n rate 32k spectrogram  -o ../images/sox-cleaned-05.png

Audio("Test-audacity.wav")

!sox Test-audacity.wav -n rate 32k spectrogram  -o ../images/sg-audacity.png

!ffmpeg -i Test.wav -filter:a "highpass=f=300" high-passed.wav -y -v quiet

Audio("high-passed.wav")

!sox high-passed.wav -n rate 32k spectrogram  -o ../images/highpass.png

Audio("test-imovie.wav")

!sox test-imovie.wav -n remix 1 rate 32k spectrogram  -o ../images/imovie.png

import mediapy

orig  = mediapy.read_image('../images/sox-sg-trimmed.png')
audacity = mediapy.read_image('../images/sg-audacity.png')
sox_21 = mediapy.read_image('../images/sox-cleaned-21.png')
sox_05 = mediapy.read_image('../images/sox-cleaned-05.png')
high_pass_300 = mediapy.read_image('../images/highpass.png')
imovie = mediapy.read_image('../images/imovie.png')




mediapy.show_images({'Original':orig, 
                     'Audacity':audacity,
                     'Sox:0.21':sox_21,
                    'Sox:0.05':sox_05,
                    'HPF:300': high_pass_300,
                    'imovie':imovie},
                    cmap='magma', columns=4, height=200 )

Original

Audacity

Sox:0.21

Sox:0.05

HPF:300

imovie

!sox test-audacity.wav output.dat

import pandas as pd
df = pd.read_csv("output.dat", skiprows=2, index_col=0, names=['values'],delim_whitespace=True)
df = df.astype('float64')

df.plot()

<AxesSubplot:>